Evaluation of Conventional Surrogate Indicators of Safety for Connected and Automated Vehicles in Car Following at Signalized Intersections

Wooseok Do, Nicolas Saunier, Luis Miranda-Moreno
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Abstract

The driving behaviors of connected and automated vehicles (CAVs) will differ from those of human-driven vehicles (HDVs) because the CAVs’ driving decisions are controlled by computers. Because of the limited amount of crash data for CAVs, researchers have relied on surrogate measures of safety to assess their safety impacts. However, they often use the same safety indicators for CAVs that were used for HDVs, raising questions about the adequacy of the safety indicators for CAVs. This study aims to investigate the suitability of using conventional safety indicators for CAVs. To achieve this, we evaluated eight safety indicators used for CAVs in the literature: time-to-collision (TTC), post-encroachment time (PET), time-exposed TTC, time-integrated TTC, deceleration rate to avoid a crash (DRAC), crash-potential index, rear-end-collision risk index, and potential index for collision with urgent deceleration (PICUD). For the evaluation, we first simulate CAVs on an approaching lane of signalized intersections using the acceleration-control algorithm. The algorithm replaces the HDV trajectories with CAVs for mixed simulations where HDVs and CAVs coexist. Analyzing the simulation output, we examined the safety indicators for the various car-following scenarios and the CAV proportions. The findings suggest that PET and PICUD can yield different safety implications for CAVs because of their small-gap car-following characteristics. Ignoring such characteristics may lead to interpreting the small-gap car-following situations as simply dangerous traffic interactions for CAVs. The car-following experiments indicate that TTC, PET, and DRAC are insufficient in measuring the safety implications when successive vehicles operate at similar speeds for CAVs.
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评估互联车辆和自动驾驶车辆在信号灯控制交叉路口跟车过程中的常规安全替代指标
联网自动驾驶汽车(CAVs)的驾驶行为将不同于人类驾驶汽车(HDVs),因为 CAVs 的驾驶决策是由计算机控制的。由于 CAV 的碰撞数据量有限,研究人员一直依靠替代安全措施来评估其安全影响。然而,他们往往使用与高清车相同的安全指标来评估 CAV,从而引发了 CAV 安全指标是否适当的问题。本研究旨在调查传统安全指标是否适用于 CAV。为此,我们评估了文献中用于 CAV 的八个安全指标:碰撞时间(TTC)、碰撞后时间(PET)、暴露时间 TTC、时间积分 TTC、避免碰撞的减速率(DRAC)、碰撞潜在指数、追尾碰撞风险指数和紧急减速碰撞潜在指数(PICUD)。在评估过程中,我们首先使用加速控制算法在信号灯路口的接近车道上模拟 CAV。在高密度车和低密度车并存的混合模拟中,该算法将高密度车的轨迹替换为低密度车的轨迹。通过分析模拟输出,我们考察了各种跟车情况下的安全指标以及 CAV 的比例。研究结果表明,由于PET和PICUD的小间隙跟车特性,它们会对CAV产生不同的安全影响。忽略这些特点可能会导致将小间隙跟车情况简单地解释为 CAV 的危险交通交互。跟车实验表明,当连续车辆以类似速度行驶时,TTC、PET 和 DRAC 不足以衡量对 CAV 的安全影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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